Systems and methods for identifying and visualizing elements of query results

ABSTRACT

The systems and methods described herein generally relate to increasing user productivity in reviewing query results by visually depicting the presence/absence of a set of query terms in a set of paragraphs across a set of documents.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application cross-references U.S. Design patent application29/438,388, for a BOXED ABACUS ICON, filed concurrently with thisnon-provisional patent application.

COPYRIGHT NOTICE

A portion of this disclosure, including Appendices, is subject tocopyright protection. Limited permission is granted to facsimilereproduction of the patent document or patent disclosure as it appearsin the U.S. Patent and Trademark Office (PTO) patent file or records,but the copyright owner reserves all other copyright rights whatsoever.

BACKGROUND

1. Field

The present specification generally relates to data analytics andvisualization of a result set.

2. Technical Background

Embodiments utilize analytics to determine document relevance as well astechniques to generate for graphical display a multi-facetedrepresentation of document relevance. The methods and systems hereinbuild on techniques for information retrieval such as TF/IDF. TF/IDF(term frequency—inverse document frequency) is a numerical statisticwhich reflects how important a word is to a document in a collection orcorpus. It is often used as a weighting factor in information retrievaland text mining. The TF/IDF value increases proportionally to the numberof times a word appears in the document, but is offset by the frequencyof the word in the corpus, which helps to control for the fact that somewords are generally more common than others.http://en.wikipedia.org/wiki/Tf%E2%80%93idf

SUMMARY

It should be appreciated that various configurations and combinations ofthe following embodiments may be deployed while still embodying theprinciples disclosed herein.

In one embodiment, a system is configured to facilitate review of a setof document search results comprising at least one computer readablestorage medium and at least one computer machine. The computer readablestorage medium includes a database management system which is storedthereon and configured to access a corpora of electronically storedcontent either directly or indirectly. A computer machine is configuredto receive a query request, comprising a set of two or more searchterms, as a computer machine input; search said corpora for a set of atleast two documents relevant to said query request; score a set ofparagraphs associated with said set of at least two documents, using analgorithm which calculates a measurement of term density versus termdiversity in each paragraph in said set of paragraphs; and rank said setof paragraphs based on said scoring step. A computer machine (possiblythe same one which received the query request although the system couldalso be configured in a distributed environment) is configured togenerate at least one interactive graphical user interface (GUI) todisplay at least one visually coded graphic to indicate whether each ofsaid two or more search terms is present in a subset of said set ofparagraphs.

In another embodiment, the subset of said set of paragraphs comprises apreset number of paragraphs corresponding to a minimum score.

In another embodiment, the subset of said set of paragraphs comprises apreset number of paragraphs wherein the preset number of paragraphsranked higher than the rest of the paragraphs from the set ofparagraphs.

In another embodiment, a first visually coded graphic is generated for afirst document in said set of at least two documents and a secondvisually coded graphic is generated for the second document in the setof at least two documents.

In another embodiment, the algorithm assigns a higher score to a subsetof said set of paragraphs with a greater term density.

In another embodiment, the algorithm assigns a higher score to a subsetof said set of paragraphs with a greater term diversity.

In another embodiment, the algorithm includes a weighting factor for aterm diversity variable and a weighting factor for a term densityvariable.

In another embodiment, the algorithm includes a weighting factor for aterm diversity variable and a weighting factor for a term densityvariable. A preset number of paragraphs, ranked higher than the rest ofthe paragraphs from said set of paragraphs, comprises said subset ofsaid set of paragraphs. The preset number of paragraphs are surfaced insaid at least one visually coded graphic.

In another embodiment the at least one visually coded graphic comprisesa set of tiles wherein each row in said set of tiles represents asurfaced paragraph based on said scoring step.

In another embodiment each search term from said two or more searchterms is assigned a graphical indicator; each paragraph in said subsetof said set of paragraphs is assigned to a vertical line in a set ofvertical lines; and wherein said visually coded graphic includes adepiction of said graphical indicators on each vertical linecorresponding to the presence of said search term in said paragraph.

In another embodiment, the at least one visually coded graphic comprisesa boxed abacus icon.

In another embodiment, a method facilitates review of a set of documentsearch results by performing steps including: receiving, as a computermachine input, a query request wherein said query request comprises twoor more search terms; accessing a corpora of electronically storedcontent either directly or indirectly on at least one computer readablestorage medium; searching said corpora for a set of relevant documents;scoring a set of paragraphs, within said set of relevant documents,using an algorithm which calculates a measurement of term density versusterm diversity for each of said set of paragraphs; ranking said set ofparagraphs based on said measurement; and generating for graphicaldisplay: a legend correlating a visually coded graphical indicator witheach search term; a list of a subset of said set of relevant documents;and an icon, for each document in said list of relevant documents,summarizing whether a search term is present in a preset number ofparagraphs associated with said document.

In another embodiment, the icon in the method is a boxed abacus icon.

In another embodiment, the icon in the method is a tile bar icon.

In another embodiment, the method further includes, if a paragraph,associated with a document that was not included in said subset ofrelevant documents, receives a higher score, in said ranking step, thanany paragraph in said subset of relevant documents, inserting saiddocument into said subset of relevant documents.

In another embodiment, a computer readable medium comprising computerexecutable instructions for execution by a computer machine tofacilitate review of a set of document search results that whenexecuted: receives a query request comprising two or more search terms;accesses a corpora of electronically stored content either directly orindirectly; searches a corpora for a set of candidate documents; scoresa set of paragraphs associated with said set of candidate documentsusing an algorithm which calculates a measurement of term density versusterm diversity; ranks said set of paragraphs based on said measurement;generates for graphical display at least one boxed abacus icon for eachof a subset of said set of candidate documents wherein a visually-codedgraphical indicator is associated with each search term and displayed ona line associated with a given paragraph from a subset of said set ofparagraphs to indicate if said search term is present in said givenparagraph. In an embodiment, the visually-coded graphical indicator maybe unique.

In another embodiment, the boxed abacus icon is linked to a set ofunderlying content associated with each paragraph depicted in said boxedabacus icon and wherein said boxed abacus icon may be clicked through todisplay said set of underlying content.

In another embodiment, the search terms are highlighted in a display ofsaid set of underlying content.

In another embodiment, the subset of said candidate documents is chosenbased on the documents containing the highest scoring paragraphs whensaid paragraphs are ranked.

In another embodiment, each boxed abacus icon includes only oneparagraph from each document in said subset.

These and additional features provided by the embodiments describedherein will be more fully understood in view of the following detaileddescription, in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments set forth in the drawings are illustrative and exemplaryin nature and not intended to limit the subject matter defined by theclaims. The following detailed description of the illustrativeembodiments can be understood when read in conjunction with thefollowing drawings, where like structure is indicated with likereference numerals and in which:

FIG. 1 is an embodiment of an exemplary interface generated forgraphical display integrating a set of boxed abacus icons by depictingsearch terms within a set of paragraphs identified from a result set ofdocuments responsively selected from a query of search terms.

FIG. 2 is an embodiment of an exemplary interface generated forgraphical display providing a matrix of boxed abacus icons for a set ofresults responsively produced from a query of search terms.

FIG. 3 is an embodiment of an exemplary interface generated forgraphical display wherein a set of tile bars is integrated into a searchresult set of a third party search engine.

FIG. 4 is an exemplary process flow for one embodiment of the system.

DETAILED DESCRIPTION

Embodiments described herein generally relate to increasing userproductivity in reviewing query results. An embodiment provides a systemand method to determine and then visually depict the presence/absence ofa set of query terms in a set of paragraphs across a set of documents.Alternative embodiments may rank a set of paragraphs in a singledocument. Embodiments may include two, three, or more paragraphvisualizations for two or more documents.

Embodiments allow a user to identify parts of a given text documentincluded in a collection returned from a query. Embodiments of thesystem and method allow a user to make judgments about documents withoutspending extra time viewing unnecessary elements. Embodiments provideresults in a display of information to enable users to assess resultsthrough the simultaneous display of programmatically determined documentrelevance (e.g., through the use of tools which order relevance based onan analysis of document metadata (e.g., Lexis Advance) or othercommercially available analysis tools including, but not limited to,sentiment analyzers) with a complimentary indication of relevance basedon user query terms and the flexibility of visualization design toprovide a richer interface for assessing query results.

An embodiment provides a visualization of a pattern of query termscontained in each document analyzed. In an embodiment, a user maycontrol the order of the documents in the results display (e.g., basedon a paragraph analysis which ranks documents with higher scoringparagraphs at the top of the list). A user may also control the order ofthe icons displayed (e.g., based on order of appearance in a document,based on a relevance score generated for each paragraph represented byan icon—such as a term-rich paragraph ranking higher than a term-poorparagraph, etc.). Embodiments allow a user to associate a visualindicator of a term's existence to the manifestation of the term in theidentified paragraph.

Embodiments of the various visualizations may be programmed to fit avariety of graphical output mechanisms whether a desktop monitor,laptop, smart phone or other graphical media. Embodiments may adjust thesizing of the visualization to accommodate full screens or smallerpop-up displays. Orientation may also be adjusted to accommodate anembodiment displayed on a particular type of media. Embodiments may beinstalled to specially code a general purpose computer machine and runfrom computer executable instructions encoded on a tangiblecomputer-readable medium.

DEFINITIONS

“Automatically” includes to the use of a machine to conduct a particularaction.

“Boxed Abacus Icon” includes a configurable icon generated for graphicaldisplay on a computerized visual output (e.g., screens associated with avariety of graphical output mechanisms), based on a result determined byexecuting an algorithm which assesses term density and/or diversity (theweighting of each may be individually set as well), comprising two ormore vertical lines, wherein a line represents a paragraph disposedwithin a document, and a set of coded (e.g., color, shaded, shaped)boxes or indicators aligned upon said vertical lines wherein each boxrepresents an associated query term's presence in said paragraph. Inanother embodiment, the orientation of the lines may be reversed. Inanother embodiment, the axis variables may be transposed. An exemplaryembodiment of a Boxed Abacus Icon is depicted in FIG. 1 at 130.

“Calculate” includes Automatically determining or ascertaining a resultusing Computer Machine Input.

“Computer Machine” includes a machine (e.g., desktop, laptop, tablet,smart phone, television, server, as well as other current or futurecomputer machine instantiations) comprising a computer processor thathas been specially configured with a set of computer executableinstructions.

“Computer Machine Input” includes input received by a Computer Machine.

“Generate for Graphical Display” includes to Automatically create, usingComputer Machine Input, an object(s) to be displayed on a GUI (e.g., alisting of hyperlinks, a heat map, a dashboard comprising a table, anicon including a Boxed Abacus Icon, shading, color-coding, etc.).

“GUI” or “Graphical User Interface” includes a type of user interfacethat allows users to interact with electronic devices via images (e.g.,maps, grids, panels, icons, etc.) displayed on a visual subsystem (e.g.,desktop monitor, tablet/phone screen, interactive television screen,etc.).

“Metadata” includes a type of data whose purpose is to provideinformation concerning other data in order to facilitate theirmanagement and understanding. It may be stored in the documentinternally (e.g. markup language) or it may be stored externally (e.g.,a database such as a relational database with a reference to the sourcedocument that may be accessible via a URL, pointer, or other means).

“Smart Indexing” includes a methodology by which subject matter expertsand information professionals create vocabularies and the algorithmicrules governing the application of tags to a content item. It mayinvolve mining a data corpus to identify at least one set key terms orpossible multiple sets including terms sets of increasing granularity orspecificity to a given subject.

“Surfacing” comprises a variety of methodologies employed to madecontent stored in servers and connected to the Internet (or othernetwork system) available for further review or selection. Content madeavailable through surfacing may comprise a hierarchy ofcomputer-selectable links or other information delivered in response toa query.

“Term Density” comprises a measurement of the number of key termsappearing within a given corpus or subset of said corpus (e.g., adocument or a paragraph).

“Term Diversity” comprises a measurement of the number of different keyterms appearing within a given corpus or a subset of said corpus. Thus,a paragraph with three distinct key terms will have a higher termdiversity than a paragraph with ten key terms that are the same.

Referring to embodiments depicted in FIGS. 1-2, collections ofvisualizations may be integrated into a results display in a variety ofways including via a matrix of multiple results. This may furtherfacilitate a reviewer looking for particular term combinations. In FIG.1, a Boxed Abacus Icon (e.g., 130) may be provided for each of multipledocuments Surfaced to illustrate query term (110) distribution in agroup of identified paragraphs for each document. Each query term (110)may be colored, shaded or otherwise coded (120) to allow an end-user toidentify which terms are present in a given paragraph. The use of theBoxed Abacus Icon (e.g., 130) is an exemplary icon to surface theseresults chosen to portray the terms in an aesthetically pleasing manner.Many more iconic/graphical figures may be possible to convey the resultset. In FIG. 1, an exemplary result set (100) of three documents (thenumber of documents should not be considered limiting) is provided withterm distribution across the top five paragraphs (again the number ofparagraphs should not be considered limiting but may, in an embodiment,include at least two paragraphs for comparison purposes) in eachdocument via a Boxed Abacus Icon (130) for each document.

In FIG. 2, a legend (220), of query terms (210), may provide coding toidentify query term (210) distribution in a specific paragraph. In FIG.2, an exemplary result set of twenty icons is provided (the number oficons should not be considered limiting) with a Boxed Abacus Icon (230)displaying whether terms are present in each of the top five paragraphsretrieved for each icon. A panel (240) may be provided to display aselection of paragraphs with the highest term distribution for a givenset of query terms (210). Alternatively, the system may be configured toSurface the paragraphs with the highest term diversity. In otherembodiments a weighting algorithm may be preconfigured or user-specifiedto juxtapose term diversity versus term density. It may be appreciatedthat multiple configurations of the Boxed Abacus Icons (230) may bedesigned to suit various graphical user displays.

In an alternative embodiment, the top paragraphs may be from the samedocument. Alternatively, a result set may comprise the top paragraphsacross a group of documents so that the highest scoring paragraphs(whether from the same document or different documents) are displayedwithin the same graphical icon or visualization. In another embodiment,the paragraphs themselves may be ranked outside of their identificationwithin a result set of the top ranked documents. In such an embodiment,a toggle might exist in the results display to allow the user toevaluate either the most relevant paragraphs or merely view the topdocuments and the best paragraphs within those documents.

Referring to FIG. 3, while embodiments may generate for graphicalrepresentation a set of stand-alone results, such graphical generationmay also be embedded into search engines or other products which returnresult sets. In FIG. 3, an embodiment (300) depicts a result set from aquery (310) against a database of case law (it may be also possible tocustomize a view to show a particular number of paragraphs). A resultset for each case may be provided showing query term (310) distributionin a tile bar format (330) (one row for each paragraph) depicting acoded tile for each term present in a paragraph identified as relevant.Coding may be added to a legend/list of query terms (310) to assist inidentification of terms in a given paragraph. Selection of a specificrow may provide a pop-up window (320) detailing the coded text for eachterm present in that paragraph. It would also be possible to integratethe Boxed Abacus Icon (130/230) in place of the tile bar visualization.

In an embodiment, a search engine identifies and inspects potentiallyrelevant documents within a query-results collection. In an embodiment,an algorithm may be further configured to utilize a specialized index(e.g., Smart Indexing) to focus a result set on a set of case law (inalternative embodiments, a specialized index could be applied to othertypes of content such as scientific journals, fictional content, etc.).

Once an initial result set has been programmatically determined, contentmay be assessed for relevance based on a score derived from assessingquery term (110/210/310) density and diversity within the subjectparagraphs. It will be appreciated that embodiments of relevance rankingaccording to the methods disclosed herein may be applied to a wide arrayof subject matter including legal, medical, scientific, popular, andmore. If the term is present, a visual indicator includes a symbol (e.g.square) coded for the associated term as indicated in an accompanyinglegend. If the term is not present, that position on the visualindicator is left blank (or white) or empty (e.g., an outline of a boxis provided that is unfilled). In an example, if all query terms(110/210/310) are present in the paragraph, a visual indicator willinclude a series of non-blank symbols. The symbols for the paragraphsmay be ordered either in order of the occurrence of each paragraphwithin the document or in order of most-to-least-relevant paragraphbased on an assessment of term diversity and/or density.

In an additional embodiment, a user may select a given paragraph symbolin order to display a set of text corresponding to that paragraph in aseparate display (e.g., a separate pane or pop-up window). Query terms(110/210/310) in a corresponding paragraph window/pane may behighlighted/bolded/font-differentiated/colored/other in some way. In anembodiment, the legend may be color-coded and then the query terms mayalso be highlighted or font-colored in the color that corresponds tothat term within the visualization. In another embodiment, highly densequery terms may receive additional shading to indicate their relativefrequency to other terms in a given section.

In various embodiments, user customizations may be possible including,but not limited to, representation of a certain number of paragraphs,ordering of the icons within the visualization by various relevanceranking algorithms (e.g., term density, term diversity, or a combinationof both).

In another embodiment, a visualization may be fine-tuned to provide avariety of ways the terms may match a user's query (e.g., stringsubsets, term synonyms, etc.). A user may be able to customize theapplication to configure how closely terms must be before a match isrecognized.

In another embodiment, a visual element may be customized to representdifferent data dimensions (e.g., issues versus facts; proper nouns ofrecognized entities, etc.).

Referring to FIG. 4, an embodiment is provided for a methodology (400)to generate for graphical display a set of Boxed Abacus Icons (130/230)to indicate term density within a set of documents/paragraphs directlyreturned as a result of a query to a database or content (or viaindirect access such as to a set of pointers to such content via a setof Metadata). A system may receive a query to parse and perform adocument search (405). Using commercially available software (includingbut not limited to various search engines, Lexis Shepard's for Research,etc.) a set of candidate documents may be returned or Surfaced (410).

Continuing with the example embodiment provided in FIG. 4, for eachdocument, the system may perform paragraph extraction (415). For eachparagraph extracted, the system may highlight/extract/quantify thenumber of query terms (110/210/310) in said paragraph (420). A value maybe assigned to a total search term count as well as a count per searchterm appearing in a given paragraph (420). In alternative embodiments, ahigher score or factor may be applied where there is an exact match or asearch term appears within a headnote or associated with other keymetadata. For instance, if a search term is a “rare term” (i.e., itoccurs below a specific threshold across the entire corpus), it may havea higher score/factor associated with it. In additional embodiments, aterm appearing in (or proximate to) a highly cited paragraph/term mayhave a higher score/factor applied to it. In another embodiment, if theterm appears in a majority opinion, rather than a dissenting opinion,then it may have a higher factor/score applied to it.

These results may be used to determine the ultimate document subset orpossibly paragraph subset that will be surfaced.

A scoring algorithm may be applied to each paragraph to determine itsrelevance based on search term diversity and frequency/density withinthat paragraph (425). Scoring algorithms may be configured to weightdiversity/frequency equally or with one factoring higher than the other.An embodiment of the system may be configured to receive a user input ora system administrator as computer machine input which predetermines orconfigures a weight to be associated with each factor(diversity/frequency). Additionally, individual search terms may also beweighted so that their presence within a given paragraph may cause thealgorithm to score that paragraph more favorably (i.e., with a higherscore than a paragraph not containing that term with all other factorsbeing equal).

Based on a set of scores Calculated, an embodiment of the system mayrank the paragraphs retrieved from most relevant to least relevant (orvice versa). Based on a subset of paragraphs scored and ranked, anembodiment may correlate the highest ranked paragraphs (depending on anumber of documents that the system is ultimately configured to display. . . it may be 2, 3, 4, 5 . . . n) with their originating documents(430). Once again the display of documents and/or paragraphs may bepreset by the system or an administrator or received through a GUI viaan end-user.

Embodiments may tag or otherwise designate each matching search term ineach document (435) and/or in the highest ranked paragraphs. Documentsmay be presented based on their paragraph scores or by the originalorder of the document return set (440) from running the initial query.

A legend for query search terms may be generated and colors assigned toeach query term (445). Alternatively, the legend could be configuredearlier in the process and terms could be coded upon identification instep (420) or some point between (420) and (470).

In the example provided, the top five paragraphs (450) and theirassociated metadata are collected for display. The term frequency foreach paragraph may be generated (or may be fetched if previouslyCalculated) (460). In this embodiment, color-coding or other coding maybe applied to terms in the retrieved document/paragraphs (470). Finally,a result set may be generated in XML or other markup language for visualgeneration (475). A result set may include a Boxed Abacus Icon (130/230)with a color-coded icon depicting whether a query search term appearswithin a given paragraph or in a set number of paragraphs within a givendocument. Paragraphs may also be associated with the original query. Inanother embodiment, a tile bar may be used to represent querydistribution in a given paragraph. A result set may also include awindow displaying a set of text associated with a given paragraphwherein a set of query terms have been highlighted/color-coded accordingthe legend generated for said set of query terms (470).

While particular embodiments have been illustrated and described herein,it should be understood that various other changes and modifications maybe made without departing from the spirit and scope of the claimedsubject matter. Moreover, although various aspects of the claimedsubject matter have been described herein, such aspects need not beutilized in combination. It is therefore intended that the appendedclaims cover all such changes and modifications that are within thescope of the claimed subject matter.

The invention claimed is:
 1. A system configured to facilitate review ofa set of document search results comprising: a. at least one computerreadable storage medium on which a database management system is storedand configured to access a corpora of electronically stored contenteither directly or indirectly; b. a computer machine configured to: i.receive a query request, comprising a set of two or more search terms,as a computer machine input; ii. search said corpora for a set of atleast two documents relevant to said query request; iii. score a set ofparagraphs associated with said set of at least two documents, using analgorithm which calculates a measurement of term density versus termdiversity in each paragraph in said set of paragraphs; iv. rank said setof paragraphs based on said scoring step; and c. a computer machineconfigured to generate at least one interactive graphical user interface(GUI) to display at least one visually coded graphic, comprising a boxedabacus icon, to indicate whether each of said two or more search termsis present in a subset of said set of paragraphs wherein said subsetcomprises a preset number of paragraphs receiving higher scores, in saidranking step, than a set of paragraphs not included in said subset.
 2. Asystem, as claimed in claim 1, wherein said subset of said set ofparagraphs comprises a preset number of paragraphs corresponding to aminimum score.
 3. A system, as claimed in claim 1, wherein a firstvisually coded graphic is generated for a first document in said set ofat least two documents and a second visually coded graphic is generatedfor said second document in said set of at least two documents.
 4. Asystem, as claimed in claim 1, wherein said algorithm scores higher asubset of said set of paragraphs with a greater term density.
 5. Asystem, as claimed in claim 1, wherein said algorithm scores higher asubset of said set of paragraphs with a greater term diversity.
 6. Asystem, as claimed in claim 1, wherein said algorithm includes aweighting factor for a term diversity variable and a weighting factorfor a term density variable.
 7. A system, as claimed in claim 1, whereinsaid algorithm includes a weighting factor for a term diversity variableand a weighting factor for a term density variable; and a preset numberof paragraphs, ranked higher than the rest of the paragraphs from saidset of paragraphs, comprises said subset of said set of paragraphs; andsaid preset number of paragraphs are surfaced in said at least onevisually coded graphic.
 8. A system, as claimed in claim 1, wherein saidat least one visually coded graphic comprises a set of tiles whereineach row in said set of tiles represents a surfaced paragraph based onsaid scoring step.
 9. A system, as claimed in claim 1, wherein eachsearch term from said two or more search terms is assigned a graphicalindicator; each paragraph in said subset of said set of paragraphs isassigned to a vertical line in a set of vertical lines; and wherein saidvisually coded graphic includes a depiction of said graphical indicatorson each vertical line corresponding to the presence of said search termin said paragraph.
 10. A method to facilitate review of a set ofdocument search results comprising: a. receiving, as a computer machineinput, a query request wherein said query request comprises two or moresearch terms; b. accessing a corpora of electronically stored contenteither directly or indirectly on at least one computer readable storagemedium; c. searching said corpora for a set of relevant documents; d.scoring a set of paragraphs, within said set of relevant documents,using an algorithm which calculates a measurement of term density versusterm diversity for each of said set of paragraphs; e. ranking said setof paragraphs based on said measurement; and f. generating for graphicaldisplay: i. a legend correlating a visually coded graphical indicatorwith each search term; ii. a list of a subset of said set of relevantdocuments; iii. if a paragraph, associated with a document that was notincluded in said subset of relevant documents, receives a higher score,in said ranking step, than any paragraph in said subset of relevantdocuments, inserting said document in said subset of relevant documents;and iv. an icon, selected from the group consisting of a boxed abacusicon and a tile bar icon, for each document in said list of relevantdocuments, summarizing whether a search term is present in a presetnumber of paragraphs associated with said document.
 11. A non-transitorycomputer readable medium comprising computer executable instructions forexecution by a computer machine to facilitate review of a set ofdocument search results that when executed: a. receives a query requestcomprising two or more search terms; b. accesses a corpora ofelectronically stored content either directly or indirectly; c. searchesa corpora for a set of candidate documents; d. scores a set ofparagraphs associated with said set of candidate documents using analgorithm which calculates a measurement of term density versus termdiversity; e. ranks said set of paragraphs based on said measurement; f.generates for graphical display at least one boxed abacus icon for eachof a subset of said set of candidate documents wherein a uniquevisually-coded graphical indicator is associated with each search termand displayed on a line associated with a given paragraph from a subsetof said set of paragraphs to indicate if said search term is present insaid given paragraph and, if a paragraph, associated with a documentthat was not included in said set of candidate documents, scores higher,than any paragraph in said set of candidate documents, inserting saiddocument in said set of candidate documents.
 12. A non-transitorycomputer readable medium, as claimed in claim 11, wherein said boxedabacus icon is linked to a set of underlying content associated witheach paragraph depicted in said boxed abacus icon and wherein said boxedabacus icon may be clicked through to display said set of underlyingcontent.
 13. A non-transitory computer readable medium, as claimed inclaim 12, wherein said search terms are highlighted in a display of saidset of underlying content.
 14. A non-transitory computer readablemedium, as claimed in claim 11, wherein said subset of said candidatedocuments is chosen based on the documents containing the highestscoring paragraphs when said paragraphs are ranked.
 15. A non-transitorycomputer readable medium, as claimed in claim 14, wherein each boxedabacus icon includes only one paragraph from each document in saidsubset.